class_docstr: Large-scale distributed sampling face recognition training Module, of course sampling is an option,
  There will be no loss of accuracy in sampling in large-scale identities training tasks, uses only
  8 NVIDIA RTX2080Ti to complete classification tasks with 10 millions of identities, 64 NVIDIA
  RTX2080Ti can complete classification tasks with 100 million of identities.
  
  See the original paper:
  https://arxiv.org/abs/2203.15565
  
  Parameters
  ----------
  symbol: Symbol
      Backbone symbol.
  fc7_model: Object
      Object of margin loss.
  memory_bank: Memory bank Object.
      Object of memory bank, which maintain local class centers and their momentum.
  memory_optimizer: Optimizer object.
      The updater of memory bank, default is sgd optimizer.
  logger:
code: def prepare(self, data_batch, sparse_row_id_fn=None):
      if sparse_row_id_fn is not None:
          warnings.warn(
              UserWarning(
                  "sparse_row_id_fn is not invoked for BaseModule."
              )
          )
  
imports: import logging
  import warnings
  from collections import namedtuple
  import horovod.mxnet
  import mxnet
  import mxnet.ndarray
  import numpy
  from default import config
  from optimizer import DistributedOptimizer
label_desc: Use image node_id 9 for calling the SampleDistributeModule obj's underlying member method code with example usage: obj.prepare(data_batch, sparse_row_id_fn) without return types